How AI and Machine Learning is Impacting Enterprise Mobility?
Enterprise mobility has, for long, promised to be a channel that would allow employees to work from wherever they want and be productive. This approach would not only improve the accuracy and efficiency of workers but would also allow them to be operational without any geographical constraints. Further, the addition of artificial intelligence (AI) into enterprise mobility will deliver the high-end results to the businesses.
AI will bring a change in the operational workflow of an organization in multiple ways and departments. The impact will be noticed across areas like device management, user experience, security, and applications. However, privacy concerns will also continue to rise in the wake of these new technologies, and advanced security measures need to be employed to prevent the data from misuse.
An organization’s diverse mobile workforce will benefit to a great extent from AI, as firms would be able to apply machine learning and other cognitive technologies to gather insights into activity streams of their employees and end users. As these patterns of behavior are identified and then analyzed by the companies, they will be able to improve their operational processes and user experiences.
Enhancing Security Measures Using AI
Now, when it comes to improving an organization’s cyber security department, AI and machine learning could be leveraged in various distinct ways. Numerous IT vendors offer ML-based device threat detection to aid companies in identifying, analyzing, and mitigating risks. Furthermore, using machine learning, devices can identify and mitigate the cyber attacks, while learning from these threats to employ its knowledge to improve its threat detection ability.
Multiple other IT vendors are also using AI to help organization’s IT departments to understand all the data that is generated and gathered by their management tools. For instance, Citrix, a multinational software company, uses a centralized management offering to manage all the devices such as smart phones, tablets, laptops, and computers that enter its office’s premises. The company further executes user-behavior analytics by applying machine learning to categorize employees or client’s data from high to medium to low risks and then adjusting these risk scores as more data is fed into this analytics system.